语言模型中树状结构表达式序列化的通信瓶颈研究
The Communication Bottleneck: A Round-Trip Study of Tree-Structured Expression Serialization in Language Models
研究通过往返协议量化了语言模型在处理树状结构表达式时的信息损失，发现该通信信道具有显著的损耗性与非对称性。实验显示，模型对组合的准确率差异高达 60.4 个点，且最佳组合方案由不同模型协同完成，而非单一模型。此外，仅需约 3600 个微调样本即可显著提升开源模型的表现，使其超越 Gemini-3.1-Pro，证实了树状结构序列化是模型间传递层级信息的关键瓶颈。
AuthorsXavier Suau, Alex Ferrando de las Morenas, Luca Zappella, Samy Bengio
When language models reason in chain-of-thought or exchange free-text intermediates, they serialize structured information into natural language. How much tree-structured compositional content survives this bottleneck? We propose a round-trip protocol that answers this question empirically for tree-structured expressions. A generator converts a procedurally generated arithmetic expression into a word problem, a separate extractor recovers the expression from the word problem alone, and symbolic equivalence provides an exact oracle. Evaluating all pairwise combinations of sixteen models yields a communication matrix whose marginals separate generation quality from extraction quality. Three main findings emerge. First, the channel is lossy and asymmetric: swapping which model generates and which extracts shifts accuracy by up to 60.4 points, and the best pair reaches 92.9% by combining different models on each end rather than the same model on both. Second, at least 73.6% of round-trip failures originate at generation, and difficulty is driven by tree structure (operator count, depth, right-branching) rather than model family. Third, the channel is trainable: ∼ 3600 fine-tuning examples that share the evaluation’s operators and tree shapes lift every open-weight model above untrained Gemini-3.1-Pro, an upper bound under matched semantics. A disjoint-domain regime with new operators and vocabulary also raises every open-weight model, confirming the gain is not an artifact of matched semantics, though a gap to the frontier remains. Together these results identify tree-structured expression serialization as a primary limiting factor when models communicate hierarchical structure through natural language.
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来源：Apple Machine Learning Research · machinelearning.apple.com